Forthcoming and Online First Articles

International Journal of Social Computing and Cyber-Physical Systems

International Journal of Social Computing and Cyber-Physical Systems (IJSCCPS)

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International Journal of Social Computing and Cyber-Physical Systems (6 papers in press)

Regular Issues

  • Deep Learning-based Medical Expert System for Diabetes Diagnosis on IoT Healthcare Data   Order a copy of this article
    by Vijayaprabakaran Kothandapani, Sathiyamurthy K, Sowmya S 
    Abstract: Machine learning in IoT healthcare plays a vital role in diagnosing diseases and predicting the risk level of health by analyzing patient health records. Diabetes has been commonly seen in all age groups of peoples. Early diagnosis of diabetes and proper medication will help the patient live normally for long life. In this study, various machine learning techniques were experimented to diagnose diabetes and the results were compared. In order to diagnose diabetes from the health record of the patient, this work proposes a Deep learning-based expert system(DL-Expert sys). The DL-Expert system predicts the risk level of the patient and provides the recommendation of the diet to the patient. The experimental results illustrate that the predictive model using Deep Learning algorithm of RNN with LSTM achieves higher accuracy than Logistic Regression, Naive Bayes and Neural Network
    Keywords: Machine Learning Simple Linear regression; Multiple Linear regression; Logistic regression; Naïve Bayes; Recurrent Neural Network with GRU and Recurrent Neural Network with LSTM; Diabetic diagnosis; Medical Expert System.

  • Secure Resource Sharing in Grid System without Public Key Settings   Order a copy of this article
    by Manik Lal Das 
    Abstract: Grid system involves the collaborative use of computers, networks, devices, software, databases and interfaces maintained by multiple organizations. Grid security has attracted increasing interests from researchers, as multiple entities in Grid system require to deliver different nature of services. Grid security is primarily guided by the Grid Security Infrastructure (GSI) that uses public key cryptography (PKC) for authentication, delegation and resource sharing between the communicating entities. Although GSI provides required security properties to Grid system, PKC-based security solution requires to manage another costly infrastructure, known as public key infrastructure. Furthermore, public key operation is a computationally expensive operation in comparison to symmetric key operation, in particular for resource-constrained environments. There could be a situation where a user wants to share resource from Grid system through some resource-constrained devices such as mobile handsets, PDAs, and other electronic gadgets. Therefore, a lightweight security protocol for Grid resource sharing can provide not only intended security goals, but also flexibility, cost optimization and user convenience. In this paper, an authenticated key establishment and secure data sharing protocol is presented for Grid system security without using public key settings. The proposed protocol extends delegation of resource server and user capability to proxy agents, and revocation of the delegated power. The proposed protocol shows its security strengths and efficiency in comparisons to other related protocols.
    Keywords: Grid security; Authentication; Key establishment; Delegation; Grid security infrastructure.

  • Rule Based Anonymization against Inference Attack in Social Networks   Order a copy of this article
    by Nidhi Desai, Manik Lal Das 
    Abstract: Social networks information has been used as a powerful decision maker in various facets of society. Meticulous scrutinization of gigantic volumes of social data will not only help in discovering societal problems but also it can provide useful solutions for forecasting, government policies, business and strategic goals. Despite of its usefulness, social networks information is vulnerable to privacy concerns due to the presence of sensitive information. Many users resist in sharing their information with the fear that the information will lead to compromise of security and privacy of their data. In recent times, inference attack using rule based mining technique has pose a challenging privacy concern, particularly in social networks. In this paper, we present Rule Anonymity, a privacy model against inference attack using rule based mining techniques in social networks. The proposed model considers adversary with strong knowledge of rule generation. A rule based anonymization technique has been used in the proposed privacy model in which rule anonymity ensures strong privacy guarantee against adversary with rule mining capability. The experimental results of the proposed anonymization technique manifests the idea of rule anonymity on social dataset.
    Keywords: Social Networks; Data Privacy; Rule Mining; Inference Attack.

  • Detecting Malicious Users in the Social Networks Using Machine Learning Approach   Order a copy of this article
    by H.L. Gururaj 
    Abstract: Social networking plays a very important role in todays life. It helps to share ideas, information, multimedia messages and also provides the means of communication between the users. The popular social Medias such as Facebook, Twitter, Instagram, etc., where the billions of data are being created in huge volume. Every user has their right to use any social media and a large number of users allowed malicious users by providing private or sensitive information, which results in security threats. In this research, we are proposing an NLP technique to find suspicious users based on the daily conversations between the users. We demonstrate the behavior of each user through their anomaly activities. Another machine learning technique called SVM classifiers to detect the toxic comments in the comments blog. In this paper, the preliminary work concentrates on detecting the malicious user through the anomaly activities, behaviour profiles, messages & comment section.
    Keywords: Social networks; malicious users; Naïve bayes; NLP; Comments; Social media; SVM.

  • iCOPS: Insider Attack Detection in Distributed File Systems   Order a copy of this article
    by Riddhi Solani, Manik Lal Das 
    Abstract: Distributed File System (DFS) has been widely used in many applications. Insider attacks in DFS is a potential target that can cause problems in many applications. A malicious insider or an outsider who controls an insider could compromise application's security by exploiting the target file(s) in the system. In this paper, a scheme, named as iCOPS, is proposed to detect insider attacks in distributed file systems. The proposed iCOPS scheme consists of two algorithms -- Process Profiling and Attack Detection. The Process Profiling runs on datanode and replica nodes that provide output to namenode, whereas, the Attack Detection runs on the namenode to detect an attack that might have triggered by the Process Profiling algorithm. The analysis and experimental results of the proposed iCOPS show notable observations in detection of data alteration by insider attacks
    Keywords: Insider Attacks; Distributed Systems; HDFS; Data Modification.

  • Distributed Blockchains for Collaborative Product Designs in Smart Cities   Order a copy of this article
    by Shajulin Benedict 
    Abstract: Personal fabrication tools such as 3D printers encourage individuals or designers for rapidly creating innovative prototypes of products. Despite the surge in innovative ideas, an immediate end product realization that improves the economic growth of a society is a challenge owing to several reasons, including permission delays. This article proposes a blockchain-enabled architecture which explores the utilization of decentralized connected 3D printers and decision-makers for collaboratively fabricating and installing societal products in smart cities -- a social computing approach. The proposed approach involves designers, policymakers, city authorities, manufacturers, and residents to collectively prototype, decide, and launch products in a distributed fashion. It promotes innovations and speedy developments of products when compared to a traditional mechanism of making a futile bid to launching products. Experiments were accomplished at the IoT Cloud Research laboratory to manifest the proposed blockchain-enabled collaborative 3D printing approach. Besides, a case study, which demonstrates the collaborative 3D printing of a TajMahal miniature using the proposed architecture, was illustrated. The proposed mechanism would reap in lots of innovations and economic improvements in smart cities in the near future.
    Keywords: Blockchain; Collaborative Design; Fabrication; Hyperledger; Societal Application.